---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- tsfile
- timeseries
- synthetic
- healthcare
- hospital-operations
- operating-room
- or-utilization
- surgical-scheduling
- staffing
- workforce
- nursing-shortage
- equipment
- biomedical
- capacity-planning
- bed-management
- ed-boarding
- aha
- aorn
- nsi
- ecri
- cms-conditions-of-participation
- perioperative
- case-cancellation
- first-case-ontime
- or-turnover
- rn-vacancy
- icu-occupancy
- time-series
- format:tsfile
pretty_name: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview) (TsFile)
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: equipment
    path: equipment.tsfile
  - split: facilities
    path: facilities.tsfile
  - split: hospital_resources
    path: hospital_resources.tsfile
  - split: or_schedule
    path: or_schedule.tsfile
  - split: staffing
    path: staffing.tsfile
---

# HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview) (TsFile)

Apache TsFile version of [`xpertsystems/hlt010-sample`](https://huggingface.co/datasets/xpertsystems/hlt010-sample).

## Overview

A **fully synthetic** hospital operations dataset combining **operating room schedules**, **staffing/workforce records**, **biomedical equipment utilization**, **daily capacity metrics**, and **facility master data** across mixed facility types (academic / large community / medium community / critical access). Calibrated to AHA Annual Survey 2023, AORN benchmarks, NSI nursing data, ECRI Institute equipment data, and CMS Conditions of Participation.

- **Tables:** 5 TsFile table(s), 35,255 rows in total.
  - `equipment`: 17,461 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:01.238000 (UTC)
  - `facilities`: 3 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.002000 (UTC)
  - `hospital_resources`: 42 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.002000 (UTC)
  - `or_schedule`: 4,226 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.189000 (UTC)
  - `staffing`: 13,523 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.605000 (UTC)

## Schema (TsFile structure)

### `equipment.tsfile`

- **Time** (INT64, milliseconds) — the source `record_date` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (13): `asset_id` (string), `facility_id` (string), `equipment_class` (string), `equipment_age_yrs` (double), `utilization_rate` (double), `in_service_hours` (double), `downtime_hours` (double), `unplanned_downtime_flag` (int64), `downtime_cause` (string), `last_maintenance_date` (string), `next_maintenance_due` (string), `failure_flag` (int64), `repair_cost_usd` (double)

### `facilities.tsfile`

- **Time** (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (11): `facility_id` (string), `facility_type` (string), `teaching_status` (int64), `trauma_level` (string), `bed_count` (int64), `icu_beds` (int64), `or_suite_count` (int64), `pacu_bays` (int64), `state` (string), `region` (string), `daily_or_capacity` (double)

### `hospital_resources.tsfile`

- **Time** (INT64, milliseconds) — the source `census_date` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (40): `facility_id` (string), `day_of_week` (string), `is_weekend` (int64), `total_beds` (int64), `occupied_beds` (int64), `occupancy_rate` (double), `icu_beds_x` (int64), `icu_occupied` (int64), `icu_occupancy_rate` (double), `pacu_bays_x` (int64), `pacu_patients` (int64), `pacu_utilization_rate` (double), `ed_boarding_hours` (double), `diversion_flag` (int64), `diversion_hours` (double), `capacity_breach_flag` (int64), `surge_day_flag` (int64), `or_utilization_rate` (double), `surgical_cases_scheduled` (int64), `or_revenue_usd` (double), `or_cost_per_min_usd` (double), `total_or_minutes` (int64), `contribution_margin_usd` (double), `block_release_efficiency` (double), `staffing_adequacy_score` (double), `operational_efficiency_index` (double), `surgical_site_infection_flag` (int64), `near_miss_flag` (int64), `consent_timeout_completed` (int64), `equipment_safety_check_flag` (int64), `facility_type` (string), `teaching_status` (int64), `trauma_level` (string), `bed_count` (int64), `icu_beds_y` (int64), `or_suite_count` (int64), `pacu_bays_y` (int64), `state` (string), `region` (string), `daily_or_capacity` (double)

### `or_schedule.tsfile`

- **Time** (INT64, milliseconds) — the source `case_date` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (17): `case_id` (string), `facility_id` (string), `or_id` (string), `case_type` (string), `surgeon_id` (string), `is_emergency` (int64), `scheduled_start_min` (int64), `actual_start_min` (double), `start_delay_min` (double), `first_case_ontime_flag` (string), `scheduled_duration_min` (int64), `actual_duration_min` (double), `turnover_time_min` (double), `cancellation_flag` (int64), `cancellation_reason` (string), `block_owner` (string), `add_on_flag` (int64)

### `staffing.tsfile`

- **Time** (INT64, milliseconds) — the source `shift_date` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (10): `shift_id` (string), `facility_id` (string), `staff_id` (string), `staff_role` (string), `shift_type` (string), `hours_worked` (int64), `overtime_flag` (int64), `float_pool_flag` (int64), `agency_flag` (int64), `staff_to_patient_ratio` (double)

## Conversion notes

- One TsFile per source CSV (5 tables), each stored as a single device: the facility id, asset id, staff id and surgeon id stay ordinary FIELDs.
- Time = the daily reporting date of each table (`record_date`, `census_date`, `case_date`, `shift_date`) as INT64 milliseconds at 00:00 UTC. Several rows share a day, so they are ordered by a 1 ms step in stable source order; the calendar day is unaffected.
- `facilities` is a 3-row facility master with no date column, so its Time is a synthetic row index. No column is dropped.

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("equipment.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
```

## Source & license

- Original dataset: [`xpertsystems/hlt010-sample`](https://huggingface.co/datasets/xpertsystems/hlt010-sample)
- Source card title / publisher: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview)
- License: cc-by-nc-4.0 (as declared by the original dataset).